Systems, methods, and visualization tools for stimulation and sensing of neural systems with system-level interaction models

ABSTRACT

A computer implemented system and method generates a patient-specific model of patient response to stimulation on a neural element basis, receives user-input of target neuromodulation sites, and, based on the patient-specific model, determines which stimulation paradigm and settings, including stimulation sites, would result in the target neuromodulation, where the stimulation sites are not necessarily the same as the resulting neuromodulation sites. The system outputs a visual representation of the stimulation sites that would result in the target neuromodulation. The system monitors a system state and/or patient state and dynamically changes which stimulation program to implement based on the state.

FIELD OF THE INVENTION

Aspects of the present invention are directed to a system(s) andmethod(s) for sensing and anatomical stimulation by electrodes of animplanted leadwire, where the stimulation is programmable to beresponsive to a sensed signal. Aspects of the present invention aredirected to a system for generating a patient-specific interactionmodel, determining a relationship between stimulation paradigms,including stimulation sites, and neuromodulation sites, dynamicallychanging stimulation goals and paradigms in response to sensed changesto a state, and/or outputting visual representations of a correlationbetween candidate stimulation sites and affected anatomical regions.Aspects of the present invention are directed to a system configured toperform stimulations and sense or receive input of data concerningeffects of the stimulations to generate a patient-specific interactionmodel usable by the system to determine the sites to be stimulated forcausing neuromodulation at sites selected by a user and for outputting avisual mapping of relationships between candidate stimulation sites andthe affected neuromodulation sites. Aspects of the present invention aredirected to generating a patient-specific interaction model based ontractography analysis prior to implantation of a leadwire, the modelindicating various relationships between candidate stimulation sites andthe affected neuromodulation sites for different placements of theleadwire, the model being output for use by a user to pre-operativelydetermine an optimal implantation site for the leadwire. The system isusable both for clinical use, and for analysis and scientificinvestigation. The present invention is further related to subjectmatter of U.S. patent application Ser. No. 13/160,104, which publishedas U.S. Pat. App. Pub. No. 2012/00141580, the entire content of which ishereby incorporated by reference herein in its entirety,

BACKGROUND INFORMATION

Stimulation of anatomical regions of a patient is a clinical techniquefor the treatment of disorders. Such stimulation can include deep brainstimulation (DBS), spinal cord stimulation (SCS), Occipital NS therapy,Trigemenal NS therapy, peripheral field stimulation therapy, sacral rootstimulation therapy, or other such therapies. For example, DBS mayinclude electrical stimulation of the thalamus or basal ganglia and maybe used to treat disorders such as movement disorders such as essentialtremor, Parkinson's disease (PD), and dystonia, and other physiologicaldisorders. DBS may also be useful for traumatic brain injury and stroke.DBS may also be useful for treating depression, obesity, epilepsy, andobsessive-compulsive disorder, Tourette's Syndrome, schizophrenia, andother indications.

A stimulation procedure, such as DBS, typically involves first obtainingpreoperative images, e.g., of the patient's brain, such as by using acomputed tomography (CT) scanner device, a magnetic resonance imaging(MRI) device, or any other imaging modality. This sometimes involvesfirst affixing to the patient's skull spherical or other fiducialmarkers that are visible on the images produced by the imaging modality.The fiducial markers help register the preoperative images to the actualphysical position of the patient in the operating room during the latersurgical procedure.

After the preoperative images are acquired by the imaging modality, theyare then loaded onto an image-guided surgical (IGS) workstation, and,using the preoperative images displayed on the IGS workstation, aneurosurgeon can select a target region within the patient anatomy,e.g., within the brain, an entry point, e.g., on the patient's skull,and a desired trajectory between the entry point and the target region.The entry point and trajectory are typically carefully selected to avoidintersecting or otherwise damaging certain nearby critical structures orvasculature.

In the operating room, the physician marks the entry point on thepatient's skull, drills a burr hole at that location, and affixes atrajectory guide device about the burr hole. The trajectory guide deviceincludes a bore that can be aimed to obtain the desired trajectory tothe target region. After aiming, the trajectory guide is locked topreserve the aimed trajectory toward the target region, and a microdriveintroducer is then used to insert the surgical instrument along thetrajectory toward the target region, e.g., of the brain. The surgicalinstrument may include, among other things, a recording electrodeleadwire, for recording intrinsic electrical signals, e.g., of thebrain; a stimulation electrode leadwire, for providing electrical energyto the target region, e.g., of the brain; or associated auxiliaryguidewires or guide catheters for steering a primary instrument towardthe target region, e.g., of the brain.

The stimulation electrode leadwire, which typically includes multipleclosely-spaced electrically independent stimulation electrode contacts,is then introduced and positioned in close proximity to the tissuetargeted for sitmulation, to deliver the therapeutic stimulation to thetarget region, e.g., of the brain. An implanted pulse generator (IPG)generates electric pulses to transmit signals via the leadwire. Theleadwire can include cylindrically symmetrical electrodes, which, whenoperational, produce approximately the same electric values in allpositions at a same distance from the electrode in any plain that cutsthrough the electrode perpendicular to the central longitudinal axis ofthe leadwire. Alternatively, the leadwire can include directionalelectrodes that produce different electrical values depending on thedirection from the electrode. The stimulation electrode leadwire is thenimmobilized, such as by using an instrument immobilization devicelocated at the burr hole entry, e.g., in the patient's skull, in orderfor the DBS therapy to be subsequently performed.

The target anatomical region can include tissue that exhibit highelectrical conductivity. For given stimulation parameter settings, arespective subset of the neural elements are responsively activated. Astimulation parameter can include, for example, a current amplitude orvoltage amplitude, which may be the same for all of the electrodes ofthe leadwire, or which may vary between different electrodes of theleadwire. The applied amplitude setting results in a correspondingcurrent in the surrounding neural elements, and therefore acorresponding voltage distribution in the surrounding tissue.

After the immobilization of the stimulation electrode leadwire, theactual stimulation therapy is often not initiated until after a timeperiod of about two-weeks to one month has elapsed. This is dueprimarily to the acute reaction of the brain tissue to the introducedelectrode leadwire (e.g., the formation of adjacent scar tissue), andstabilization of the patient's disease symptoms. At that time, aparticular one or more of the stimulation electrode contacts is selectedfor delivering the therapeutic stimulation, and other stimulationparameters are adjusted to achieve an acceptable level of therapeuticbenefit. The IPGs offer a wide range of stimulation settings which canbe independently or concurrently varied in order to correspondinglyalter the size, shape, and location of the volume of tissue beingtherapeutically affected by the stimulation.

Systems and methods are provided that facilitate exploration of targetregions of stimulation and stimulation therapies to determine whichtherapy regimen is best suited for a particular patient or group ofpatients.

A treating physician typically would like to tailor the stimulationparameters (such as which one or more of the stimulating electrodecontacts to use, the stimulation pulse amplitude, e.g., current orvoltage depending on the stimulator being used, the stimulation pulsewidth, and/or the stimulation frequency) for a particular patient toimprove the effectiveness of the therapy. Parameter selections for thestimulation can be achieved, for example, via trial-and-error. However,the use of guiding visualization software provides for efficientstimulation parameter selection. See Frankemolle, A. et al., “Reversingcognitive-motor impairments in Parkinson's disease patients using acomputational modelling approach to deep brain stimulation programming,”Brain 133 (3): 746-761 (2010). Indeed, systems and methods are providedthat provide visual aids of the electrode location in the tissue mediumalong with computational models of the volume of tissue influenced bythe stimulation, thereby facilitating parameter selection. See, forexample, U.S. patent application Ser. No. 12/454,330, filed May 15,2009, which published as U.S. Pat. App. Pub. No. 2009/0287271 (“the '330application”), U.S. patent application Ser. No. 12/454,312, filed May15, 2009, which issued as U.S. Pat. No. 8,326,433 (“the '312application”), U.S. patent application Ser. No. 12/454,340, filed May15, 2009, which published as U.S. Pat. App. Pub. No. 2009/0287272 (“the'340 application”), U.S. patent application Ser. No. 12/454,343, filedMay 15, 2009, which published as U.S. Pat. App. Pub. No. 2009/0287273(“the '343 application”), and U.S. patent application Ser. No.12/454,314, filed May 15, 2009, which published as 2009/0287467 (“the'314 application”), the content of each of which is hereby incorporatedherein by reference in its entirety. Those applications describe systemsincluding equation-based models for generation of estimated volumes ofactivation (VOAs) based on input of stimulation parameters. Thedescribed systems and methods provide for estimation of stimulationvolumes and display models of a patient anatomy and/or a stimulationleadwire, via which to graphically identify the estimated stimulationvolumes and how they interact with various regions of the patientanatomy. If a physician selects a therapeutic stimulation parametercombination, the software displays a representation of the volume ofsurrounding tissue which is estimated to be activated by the system. Seealso S. Miocinovic et al., “Cicerone: stereotactic neurophysiologicalrecording and deep brain stimulation electrode placement softwaresystem,” Acta Neurochir. Suppl. 97(2): 561-567 (2007). FIG. 3 shows anexample user interface, using which a user can input and/or modifystimulator settings in the left two panels, while the right panel showsa model of anatomical structures, an implanted leadwire, and anestimated VOA.

U.S. Prov. Pat. App. Ser. Nos. 61/521,583 (“the '583 application”),filed Aug. 9, 2011 and 61/690,270 (“the '270 application”), filed Jun.22, 2012, and U.S. patent application Ser. No. 13/507,962, filed Aug. 9,2012, which published as U.S. Pat. App. Pub. No. 2013/0116744 (“the '962application”), each of which is hereby incorporated by reference in itsentirety, further describe generation of a VOA on a fiber specificbasis.

SUMMARY

According to an example embodiment of the present invention, astimulation leadwire apparatus includes a plurality of leadwiresconnected to a programming source, e.g., an implanted pulse generator(IPG) including a plurality of ports to the leadwires, where theleadwires include a multitude of electrodes. In an example embodiment,the arrangement further includes multiplexers by which to selectparticular ones of the leadwires and electrodes. The electrodes, or atleast a portion thereof, are controllable for emitting electrical pulsesto stimulate an anatomical region, e.g., the brain, of a patient in whomthe leadwire is implanted. Additionally, the electrodes, or at least aportion thereof, according to one example embodiment, are usable assensors for sensing effects of the stimulation. Because of the inclusionof a multitude of leadwires and electrodes, the electrodes can be placedat many different locations with stimulations and sensing performed atthe many locations, by which much information can be accumulated, basedon which the system, according to an example embodiment, generates apatient-specific interaction model that associates stimulation sites andtheir corresponding stimulation parameters to affected neuromodulationsites. For example, the system controls the electrodes to perform astimulation, obtains sensor readings from the electrodes, and modifiesthe stimulation parameters according to the sensor readings. The systemuses sensor information obtained over a plurality of such appliedstimulations to generate and/or update the patient-specific interactionmodel. Further, in an example embodiment, the system modifies thestimulation parameters based on the patient-specific interaction modelwhich has been updated according to the sensor information indicatingeffects of the prior stimulations. However, as explained below, thepatient-specific interaction model can alternatively or as a supplementbe generated based on other input such as a tractography map.

Further, the present invention provides example methods that simplifyparameter selection so that a system with a multitude of implantedelectrodes positioned at a plurality of anatomical locations is not toounwieldy. For example, the system uses models by which a user can selecta desired outcome at a neuromodulation effect region, and the systemselects the particular electrodes to implement and the particularsettings to use for those electrodes, so that the user need not selectparameters for each of the multitude of electrodes. Thus, the mappingsallow for implementation of such a system that includes a multitude ofelectrodes.

Example embodiments of the present invention provide a sensing andstimulation system that is configured to stimulate and sense at multiplesites of a neural system simultaneously. The system is configured forthe stimulation to be performed either in an open-loop mode or aclosed-loop mode in which the stimulation is in response to raw orprocessed sensed information. The system includes one or more modulesfor stimulating one or more anatomical regions, tissue, and/orstructure. The one or more modules are configured to simultaneouslysense in multiple sensing sites and stimulate one or more stimulationsites.

According to an example embodiment, the system is configured to obtainsensor information and/or user input for determining a state of thepatient and/or a state of the system, and, based on such information inassociation with stimulation information, generate a transfer function,also referred to herein as a patient-specific interaction model, thatassociates stimulation sites and the associated sitmulation parameterswith affected neuromodulation sites.

For example, according to an example embodiment, the system stimulates aplurality of anatomical sites, one by one, at one stimulation parameterset or at multiple stimulation parameter sets. For each suchstimulation, the system uses information obtained from sensors and/orfrom user input information regarding the effect of the respectivestimulations on the patient, thereby learning a correlation ofstimulation sites to affected neuromodulation sites, such correlationforming a patient-specific interaction model that predicts affectedneuromodulation sites for particular stimulation sites (e.g., which candiffer for different stimulation parameters). The system is configuredto use the interaction model to thereafter output a suggestedstimulation site (and stimulation parameters) in response to user inputof one or more targeted neuromodulation regions. For example, based onthe known transfer function, the system uses an optimization algorithmto determine the suggested one or more stimulation sites and one or moreparameter sets for effecting the neuromodulation at the one or moretarget sites, which may be different than the stimulation sites. Forexample, according to one example embodiment, the system initiallyassumes a linear expandability of the interaction model to obtaininitial candidates and then refines the candidate selection using anysuitably appropriate optimization, e.g., a gradient descent optimizationalgorithm.

According to an example embodiment, the system is configured tostimulate one or more sites and sense (e.g., local field potentials(LFP), action potentials at individual neurons, mean firing rate, powerat a particular frequency bands) either simultaneously or sequentiallyat multiple sites, and, according to an example embodiment, the systemis configured to modify the stimulation at the one or more sites basedon the sensing at the multiple sites. Epilepsy is one of the onlyindications where stimulation responsive to sensing has been shown tohave value. In spinal cord stimulation (SCS), sensing has also beenshown to have value (Saluda/NICTA) for managing the amplitude for apatient. Clinical evaluations are being performed, and the systemaccording to the present invention can help perform evaluations, todetermine further value in such sensing. Specifically, exampleembodiments provide for sensing at the multiple sites and/or usingsensed action potentials, mean firing rate, and/or frequency bandspecific power, as mentioned, which provide an enhanced closed-loopoperation.

In an example embodiment, the system further includes a module forvisualizing (and/or outputting a listing of) areas and/or components ofthe neural system expected to be affected by stimulation at the one ormore stimulation sites. The module is configured to determine theexpected sites based on a system model (the model can be derived frompatient-specific radiographic data, an atlas-like model, informationderived from evoked responses to stimulation as measured by the system,information compiled from similar data into an average-patient model, orinformation derived from PET, MRI, or other functional imaging data).

The system, according to example embodiments, includes algorithms forautomated determination of interaction models, and to automaticallyderive stimulation solutions to a user defined stimulation goal, e.g.,stimulation of areas X and Y and inhibition of A and B to achieve aparticular goal) (open-loop or closed-loop in response to an observedsignature (e.g., LFP, mean firing rate, etc.)). Further in this regard,the use-input goal can be to produce certain neuromodulation effectsand/or to reduce neural activity in one or more regions. For example, asensor might sense neural activity in one or more regions which neuralactivity a clinician can input should be reduced. The system can thenuse an optimization algorithm to output at which one or more regions toperform a stimulation, and with which stimulation parameters, to mosteffectively achieve the indicated neuromodulation at the indicated sitesand to most effectively reduce the indicated neural effects (e.g., atthe indicated regions).

Further, the system is highly scalable, and therefore can be used tointeract with complex multi-site neural systems (of which type mostneural systems are), and for new and improved therapies in response toadvancements in spatio-temporal interaction with the nervous system.

According to example embodiments of the present invention, a leadwireincludes more than 16 electrodes and the system is configured to controlstimulation using such a leadwire. According to an example embodiment,the system is scalable to provide, control, and monitor hundreds ofelectrodes.

According to an example embodiment, the system provides a neural systemvisualization aid, which can help a clinician adjust the complexstimulation program.

According to an example of the present invention, a computer-implementedmethod includes receiving, by a computer processor, user input selectinga neuromodulation effect region of a patient; and responsive to theinput, determining, by the processor, electrode neuromodulation settingsestimated by the processor to, when applied to an implanted electrodeleadwire, produce a volume of tissue activation that at least partiallyencompasses at least one target anatomical stimulation candidate regionwhich is mapped by the processor to, and is at a distance from, theselected neuromodulation effect region.

According to an example aspect of the method, the leadwire is implantedin the patient's brain. Alternatively or additionally, the at least onetarget anatomical stimulation candidate region includes a plurality ofdistinct regions. Alternatively or additionally, the user inputspecifies the neuromodulation effect region as the region at whichneural activity is to be reduced. Alternatively or additionally, themapping is based on a susceptibility weighted imaging (SWI) image.Alternatively or additionally, the mapping is based on a probabilistictractography model. Alternatively or additionally, the mapping includesa patient-specific mapping that is specific to the patient and that isbased on the response information indicating prior responses by thepatient to previous neuromodulations.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, theselected neuromodulation effect region is one of a plurality ofneuromodulation effect regions selected by the user input, and thedetermining includes determining one or more sets of neuromodulationsettings to be distributed to one or more electrodes of the leadwire toproduce a selected therapeutic effect at the plurality ofneuromodulation effect regions.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, the mappingperformed by the processor is performed by extrapolating new stimulationparameters from modeled stimulation parameters that are mapped in themodel to neuromodulation effect regions, which stimulation parametersare estimated, by the processor and based on the model, to produce aselected effect at the selected neuromodulation effect region, theselected effect at the selected neuromodulation effect region not beingmapped in the model.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, the methodfurther includes generating the mapping, and the generation of themapping includes: performing a plurality of neuromodulations at arespective plurality of sets of neuromodulation settings; and recording,for each of at least a subset of the neurmodulations, at least oneneuromodulation effect occurring at a respective one or more anatomicalregions, the one or more anatomical region being responsively mapped bythe processor to a respective one or more stimulation regions of tissueestimated by the processor to have been activated by the respectiveneuromodulation.

According to an example further aspect of the immediately precedingaspect, the mapping is the patient-specific mapping and the plurality ofneuromodulations are performed on the patient.

According to an example further aspect of either of the two immediatelypreceding aspects, at least some of the neuromodulation effects aresensed by electrodes of the leadwire.

According to an example further aspect of the immediately precedingaspect, the plurality of neuromodulations are performed by electrodes ofthe leadwire different than the electrodes that sensed the at least someof the neuromodulation effects. Alternatively, each of at least a subsetof the electrodes of the leadwire are configured to both perform aneuromodulation and sense an effect of a neuromodulation.

According to an example further aspect of any of the preceding aspectsaccording to which neuromodulation effects are recorded, at least someof the neuromodulation effects are input by a user.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, theleadwire includes more than 16 electrodes; the leadwire is controlledvia an implanted pulse generator that is configured to cause 32electrodes of the leadwire to simultaneously output an electricalstimulation pulse; and/or the leadwire is one of a plurality ofleadwires that are all controlled via an implanted pulse generator thatis configured to cause 32 electrodes of the plurality of leadwires tosimultaneously output an electrical stimulation pulse.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, the methodfurther includes controlling electrodes of the implanted leadwire toperform a neuromodulation therapy according to the determined settings;obtaining sensor readings from the electrodes; and responsive to thesensor readings, modifying the stimulation settings.

According to an example further aspect of the immediately precedingaspect, the method further includes receiving over time a plurality ofuser input of clinical states; and correlating, by the processor, theuser input clinical states to biopotential signatures sensed byelectrodes at times to which the clinical states respectivelycorrespond. Further, the sensor readings, responsive to which thesettings are modified, indicate respective biopotential signatures, andthe modification is based on the correlation.

According to an example further aspect of the aspect according to whichthe stimulation settings are modified responsive to the sensor readings,the sensor readings: indicate a mean firing rate of neural elements, themodification being based on the mean firing rate; are power readings ata specified frequency band; and/or are of local field potentials.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects, theleadwire is one of a plurality of leadwires, each of the leadwiresincluding at least one respective electrode controlled by an implantedpulse generator (IPG), the IPG including a plurality of ports; and atleast one multiplexer is provided between a respective one of the portsand a respective subset of more than one of the plurality of leadwires,which multiplexer is configured to select between the subset of the morethan one of the plurality of leadwires for application to the electrodesthereof, of a signal from the respective port, the signal causing theelectrodes thereof to produce electrical pulses for neuromodulation oftissue in which the subset of leadwires is implanted.

According to an example of the present invention, a computer-implementedmethod includes controlling electrodes of an implanted leadwire toperform a neuromodulation therapy according to a set of stimulationparameters; obtaining sensor readings from the electrodes; andresponsive to the sensor readings, modifying the stimulation parameters.

According to an example aspect of the method, the method furtherincludes receiving over time a plurality of user input of clinicalstates; and correlating, by the processor, the user input clinicalstates to biopotential signatures sensed by electrodes at times to whichthe clinical states respectively correspond. Further, the sensorreadings, responsive to which the stimulation parameters are modified,indicate respective biopotential signatures, and the modification isbased on the correlation.

According to an example aspect of the method, which can be provided incombination with the preceding aspect, the method further includesreceiving user input of a plurality of states, and, for each of theplurality of states, a respective set of stimulation parameters to beapplied responsive to sensing of the respective state, where at leastone of the states is defined according to a reading of a non-implantedsensor; time-correlating, by the processor, readings of the electrodesof the leadwire to readings of the non-implanted sensor; andsubstituting, by the processor, the definition of the at least one ofthe states with an alternate definition of a state according to areading of the electrodes of the leadwire; where the modifying isperformed based on the alternate definition.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects of themethod, the modification is based on sensor readings of a plurality ofthe electrodes positioned at a respective plurality of anatomicallocations.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects of themethod, the sensor readings indicate a mean firing rate of neuralelements, the modification being based on the mean firing rate.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects of themethod, the sensor readings include power readings at a specifiedfrequency band.

According to an example aspect of the method, which can be provided incombination with any one or more of the above-noted aspects of themethod, the sensor readings include readings of local field potentials.

According to an example embodiment of the present invention, astimulation system includes an implanted pulse generator (IPG) includinga plurality of ports; a plurality of leadwires, each of the leadwiresincluding at least one respective electrode; and at least onemultiplexer between a respective one of the ports and a respectivesubset of more than one of the plurality of leadwires, which multiplexeris configured to select between the subset of the more than one of theplurality of leadwires for application to the electrodes thereof, of asignal from the respective port, the signal causing the electrodesthereof to produce electrical pulses for neuromodulation of tissue inwhich the subset of leadwires is implanted.

The various components and methods described herein may be practicedand/or provided, each alone, or in various combinations.

An example embodiment of the present invention is directed to aprocessor, which can be implemented using any conventional processingcircuit and device or combination thereof, e.g., a Central ProcessingUnit (CPU) of a Personal Computer (PC) or other workstation processor,to execute code provided, e.g., on a hardware computer-readable mediumincluding any conventional memory device, to perform any of the methodsdescribed herein, alone or in combination. The memory device can includeany conventional permanent and/or temporary memory circuits orcombination thereof, a non-exhaustive list of which includes RandomAccess Memory (RAM), Read Only Memory (ROM), Compact Disks (CD), DigitalVersatile Disk (DVD), and magnetic tape.

An example embodiment of the present invention is directed to a hardwarecomputer-readable medium, e.g., as described above, having storedthereon instructions executable by a processor to perform the methodsdescribed herein.

An example embodiment of the present invention is directed to a method,e.g., of a hardware component or machine, of transmitting instructionsexecutable by a processor to perform the methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a sensing and stimulation device, according to anexample embodiment of the present invention.

FIG. 2 illustrates a sensing and stimulation device with multiplexingmodules, according to an example embodiment of the present invention.

FIG. 3 is a conventional user interface for setting and visualizingstimulation programs.

FIG. 4 shows an example display of a user interface showing atractographical map and stimulation model according to an exampleembodiment of the present invention, where, although not shown in thegrayscale image, different displayed neural element are displayed indifferent colors, where the different colors represent a correspondingneuromodulation effect.

FIG. 5 shows a probabilistic patient-specific tractography-based modelshowing an expected neuromodulation effect at sites A-C (columns in theillustrated chart) for stimulations at respective ones of the sites A-C(rows in the illustrated chart), according to an example embodiment ofthe present invention.

FIG. 6 shows a probabilistic patient-specific biopotential-based modelshowing an expected neuromodulation effect at sites A-C (columns in theillustrated chart) for stimulations at respective ones of the sites A-C(rows in the illustrated chart), according to an example embodiment ofthe present invention.

FIG. 7 shows an example user interface display that graphicallyidentifies areas likely to be affected by a particular stimulationparadigm, according to an example embodiment of the present invention.

FIG. 8 shows an example data flow for the system to propose astimulation paradigm and associated parameters in response to userinput, according to an example embodiment of the present invention.

DETAILED DESCRIPTION

According to an example embodiment, a system includes at least oneactive stimulation/sensing module with a power source.

According to an example embodiment, the system includes more than onemodule, providing scalability. A plurality of the modules can be poweredby a primary module or each can have its own power source. In an exampleembodiment, the multiple modules are interconnected by wires (or anothertangible connection). Alternatively, the modules are wirelesslyconnected. Still further, according to an example embodiment, the systemincludes both wired and wireless connections between the modules.

According to an example embodiment, the system includes and managesstimulation and sensing of one or more leadwires that include more than16 sensing/recording electrodes. A multiple current sources architecture(as opposed to a single voltage or current source for all of theelectrodes) is the optimal stimulation architecture because it providesthe best opportunity for stable stimulation, and enables fine tuning ofstimulation at one or more stimulation sites.

According to example embodiments of the present invention, the system isconfigured to record at multiple locations simultaneously (or nearlysimultaneously via high sampling rates) and stimulate at least one site.Sensed data from the multiple sites can be processed together to drivestimulation at the one or more sites.

Thus, for example, according to an example of the present invention, amethod includes controlling electrodes of an implanted leadwire toperform a neuromodulation therapy according to a set of stimulationparameters; obtaining sensor readings from the electrodes; andresponsive to the sensor readings, modifying the stimulation parameters.Further, according to an example, the modification is based on sensorreadings of a plurality of the electrodes positioned at a respectiveplurality of anatomical locations. Below, a leadwire system particularlysuitable for use to perform the sensing and stimulating is described,where the system includes electrodes positioned at many anatomicallocations for sensing and/or stimulating.

The system also supports stimulation programmed in an open-loop mode.The system also supports sensing in an open-loop mode, where the systemprovides the user with sensed data (raw or processed) via a userinterface, which data can help the user know if an electrode is in theright location (e.g., via knowledge of evoked potential signatures), andotherwise help the clinician/physician interact with the neural system.Note that the stimulation response can be programmed to occur in one ormultiple places that can, but need not, coincide with the sensingsite(s).

The utility of a system with such complexity is greatly enhanced byvisualization and algorithms. Thus, according to example embodiments ofthe present invention, the system provides visualization componentswhich help the physician to estimate how stimulation at one site affectsother sites. According to an example embodiment, the system performs anestimation of stimulation effect (which it visualizes) based onpatient-specific data, such as a probabilistic tractography model basedon a diffusion tensor imaging (DTI) or susceptibility weighted imaging(SWI) image. According to an example embodiment, the tractography modelis combined with a computational model of stimulation so that the effectof stimulation parameters (e.g., pulse width (PW), amplitude, rate,field configuration in terms of, e.g., anode/cathode and percentages) isincluded in the visualized estimate. In other words, a computationstimulation model is used to determine an electric field, activationthresholds, and therefore an immediate estimated activation region, andthen the tractography model is applied to the computation stimulationmodel to determine which neural elements are in the immediate estimatedactivation region, the system following the extension of such neuralelements to determine outlying sites which would be affected by thoseneural elements. In an example embodiment, the computational modelincludes linear (e.g., a simple model that fits definition of linearsystems) or non-linear (e.g., including a differential equation and asolution thereof) models of representative neural elements.

According to an example embodiment, in instances where apatient-specific tractography data set is not available, the system isconfigured to register another tractography model or atlas (e.g., apatient population model or atlas that is registered to the patient) tocreate the estimate for a given patient. Other radiographic elements canbe used to contribute to the interaction model (e.g., functional imagingsuch as fMRI, PET, EEG, MEG, etc.). For example, according to an exampleembodiment, the system generates a tractography model based on MRimaging.

In an example embodiment, the system allows the user to select points inthe brain where neuromodulation is desired, and executes an algorithmthat identifies candidate stimulation sites that are likely to affectone or more of the brain areas that the user desires to neuromodulate.In an example embodiment, the system produces an output for thecandidate sites to be visualized by the user, and uses different colors(or other visual properties) to identify locations connected to a givenbrain area that the user has identified as a neuromodulation target. Oneadvantage of the tool is that locations can be identified that arelikely to enable neuromodulation of multiple candidate targets, i.e.,“nodes” that represent candidate surgical and stimulation targets forachieving the input goals, which can include neuromodulation targetsand/or sites at which certain neural activity is to be inhibited. Thesetools are likely to be helpful pre-operatively, when the clinician isworking to identify stimulation targets for a specific patient.

Thus, according to an example, a method includes receiving, by acomputer processor, user input selecting a neuromodulation effect regionof a patient; and responsive to the input, determining, by theprocessor, electrode neuromodulation settings estimated by the processorto, when applied to an implanted electrode leadwire, produce a volume oftissue activation that at least partially encompasses at least onetarget anatomical stimulation candidate region which is mapped by theprocessor to, and is at a distance from, the selected neuromodulationeffect region. In an example, the user-selected points can be at aplurality of distinct regions, which the processor determines would beaffected by one or more direct stimulation sites.

As mentioned above, the mapping between the user-selected region(s) andthe stimulation candidate region(s) can be based on a SWI image and/or aprobabilistic tractography model.

Further, according to an example embodiment, the mapping can be based onresponse information indicating prior responses by the patient toprevious neuromodulations. For example, according to an exampleembodiment of the present invention, the system includes sensors forsensing patient responses to stimulation, and the system is configuredto create a patient-specific neural site interaction model of expectedactivation and inhibition based on the sensed information. For example,the system uses a stimulation paradigm (which can include variousparameters, waveforms, and/or temporal patterns) to perform astimulation and evaluates a response to the stimulation using somecharacteristic, e.g., the power in a certain part of the spectra of therecorded signal. (The paradigm used can be manually selected or can bepreprogrammed into the system based on an understanding of bestpractices.) The evaluated paradigm can include multi-site stimulation.Thus, example embodiments provide a system that generates apatient-specific response model based on biopotential responses tostimulation, tractography, fMRI, PET, source-localization (i.e., usingrecordings, estimating sources that caused sensed signals), and/or otherdata.

In an example embodiment, the system includes an automated algorithm forstimulating a defined set of sites, one at a time (and, according to anexample embodiment, repeatedly with different parameters), e.g., usingcurrent steering (see, e.g., U.S. Prov. Pat. App. Ser. No. 61/753,232,filed Jan. 16, 2013 and U.S. patent application Ser. No. 14/011,870,filed Aug. 28, 2013, which published as U.S. Pat. App. Pub. No.2014/0066999, the entire contents of each of which is herebyincorporated by reference in its entirety), and sensing the evokedresponses to the stimulation at one or more of the defined set of sites,to create an interaction model specific to that patient. The automatedalgorithm, causing the processor to trigger the pulses for thestimulation, can be executed under the watchful eye of a clinician, andcan include causing brief stimulations where response latencies permit.

The sensed responses, and therefore the sensing, can be at sites otherthan those being stimulated. In other words, stimulation at location Acan cause a response at location B. According to an example embodiment,the algorithm includes automated evaluation of simultaneous multi-sitestimulation and sensing at one or more sites, to expand the interactionmodel. The number of multi-site stimulation combinations may be large,and automatic testing of all of the possible combinations can beinefficient. Therefore, according to an example embodiment, the systemgives the user the capability to choose specific types of stimulation(e.g., only anodic monopolar stimulation, only cathodic monopolarstimulation, with particular pulse widths, certain specified frequenciesof pulses, etc.) to include for the algorithmic automated evaluation,and is also given the capability to manually prescribe specificstimulation configurations to test and add to the neural systeminteraction model.

According to an example embodiment, the system provides an “inverse”capability, whereby the system obtains user-input of an identificationof a desired system-level response to stimulation (e.g., excite one ormore neural areas, and/or inhibit one or more neural areas), and thesystem executes an algorithm to do one or more of the following: (1)evaluate stimulation paradigms (via evoked responses sensed by thesystem), search the stimulation parameter space (stimulation sites,parameters such as PW, rate, amplitude, configuration, andspatio-temporal stimulation paradigms between sites) for a stimulationparadigm that achieves the user-described stimulation objective, andoutput the determined paradigm with stimulation parameters to the user,i.e., the system iterativly tries new paradigms and parameters until thesystem achieves or gets close to the input goal, which can be determinedbased on sensor and/or user input data; (2) use the interaction model(radiographically-derived and/or evoked potential-derived, as describedabove) and determine and provide to the user a stimulation paradigm(could be single-site or multi-site with independent stimulationparameters for each site, and the spatio-temporal relationship ofstimulation, i.e., when to stimulate where, may be part of theparadigm), i.e., use the interaction model to estimate which stimulationsites and settings would result in the desired goal; and (3) provideinformation, such as a visualization of the stimulation to responsemapping, the interaction matrix, etc., to the user for evaluating theparadigms and searching for stimulation parameters. Note that forparameter spaces that are particularly vast, and/or for systems that arenonlinear, genetic optimization algorithms and/or support vectormachine-based algorithms are good candidates for use for selecting astimulation paradigm and associated parameters.

Thus, according to an example, the processor generates the model byperforming a plurality of neuromodulations at a respective plurality ofsets of neuromodulation settings; and recording, for each of at least asubset of the neurmodulations, at least one neuromodulation effectoccurring at a respective one or more anatomical regions, the one ormore anatomical region being responsively mapped by the processor to arespective one or more stimulation regions of tissue estimated by theprocessor to have been activated by the respective neuromodulation.However, in an example, the mapping performed by the processor between atarget anatomical stimulation candidate region and user-selectedneuromodulation effect region is performed by extrapolating newstimulation parameters from modeled stimulation parameters that aremapped in the model to neuromodulation effect regions, which stimulationparameters are estimated, by the processor and based on the model, toproduce a selected effect at the selected neuromodulation effect region,and the selected effect at the selected neuromodulation effect regionneed not be mapped in the model its elf.

For example, FIG. 6 shows an example patient-specific interaction model,where the rows represent the stimulation sites and the columns representthe resulting neuromodulation effect at various sites. For example, rowA shows that a stimulation at a particular parameter set at site Aproduces a change in a particular neuromodulation effect (e.g., localfield potential (LFP), power at a particular frequency band, and/orneuro-activation spike) at each of sites A, B, and C, where the changeis minimal at sites A and B (0.01 and 0.09, respectively) but larger atsite C (0.52). Rows B and C similarly show changes occurring at sitesA-C in response to stimulations at sites B and C, respectively.

In an example embodiment, the system initially applies assumptions oflinearity of those relationships with respect to stimulation parameters(an increase in stimulation amplitude, for example, is assumed to causea linear increase in the indicated changes) and/or with respect tocombinations (stimulation at sites A and B assumed to produce anadditive effect of the stimulations at sites A and B individually).After obtaining candidate stimulation sites and parameters based on themodel with the linearity assumptions, the system applied a suitablyappropriate optimization algorithm, e.g., a conventionally knowngradient descent optimization routine or variation thereof, to furtherrefine the suggested site(s) and parameter(s) to be used for achievingthe target neuromodulation.

FIG. 8 shows a data flow of a method for outputting suggestedstimulation sites and/or parameters for input target neuromodulationsites. FIG. 8 shows that a user defines a stimulation objective, e.g.,to increase and/or reduce neural activity in one or more respectiveregions. In the example shown in FIG. 8, the user inputs a target of amodest increase in a particular defined characteristic (there can be anynumber of characteristics, such as LFP or spikes) at site A, no changeat site B, and a robust inhibitory change at site C. The system thenresponsively performs an inverse optimization algorithm, as described,to determine suggested stimulation sites and/or parameters to achievethe input target neuromodulation effect at the indicated target sites,and then outputs the determined sites and/or parameters as a suggestionto the user.

It is noted that the patient-specific interaction model (alone or incombination with the optimization algorithm) can indicate multiple waysby which to produce the targeted neuromodulation, but it can occur thatone way involves stimulation at more sites than another way, in whichcase, according to an example embodiment, the system takes this intoconsideration when suggesting a stimulation paradigm, stimulation atfewer sites being considered more optimal all else being equal.

According to an example embodiment, the development of interactionmodels and the determination of the solution to the “inverse” problemare automated so that the physician/clinician/user would only define thesystem-level stimulation goal, and the system would automaticallydetermine and output to the user a desired stimulation paradigm (whichmay be very complex).

In an example embodiment of the present invention, the system outputs auser interface display that shows the user which parts of the neuralsystem likely are able to be affected (an ability-to-impact model), forexample, based on the interaction models. Such output can give the usera sense of constraints when defining the stimulation goal (for a givenimplanted system). When the interaction model is non-invasively derived,e.g., via tractography, functional imaging, etc., this output can behelpful for surgical planning, where the ability-to-impact model isspecific to candidate electrode sites. For example, the system canoutput the ability-to-impact model for each of a plurality of leadwiresites input by the user, and provide the ability-to-impact model foreach of the input leadwire sites. The user can then select whicheversite provides the best possible coverage.

According to an example embodiment, the user can define a stimulationgoal and set the system to run in an open-loop mode. The user can alsoalternatively set a stimulation goal to dynamically change responsive toa sensed system state. For example, the user can define one or moresensed system states, and one or more respective neural systemstimulation goals (with corresponding stimulation paradigms), and definewhich stimulation paradigm is triggered for a given sensed system state.

The system provides tools for the user to define a sensed system state.The state can be defined by feedback parameters including, for example,sensor data (e.g., of external sensors wirelessly connected to thesystem) and/or user feedback (e.g., patient feedback input via a remotecontrol). For example, according to an example embodiment, the power orchanges in power in parts or all of the measured frequency spectrum atspecific sites (e.g., the beta power in area A is high, and the gammapower in area D is also high) is one way of defining a sensed systemstate. Additionally or alternatively, the user can set the system toperform time-domain analyses for obtaining a system state on which basisto dynamically change the stimulation goal. Such analyses can include,for example, wavelet analysis, statistical measures, properties ofsingle-unit firing patterns, i.e., firing patterns of action potentialsof a single neuron, (e.g., inter-spike-interval relationships, i.e., thetime between action potential and the patterns of such intervals, and/orother relationships concerning a neuron measureable with smallelectrodes).

A non-exhaustive list of example sensors used for system operation(based on the raw or processed data from the sensors) includesbio-potential sensor, chemical (e.g., neurotransmitter) sensors,temperature sensors, and physical sensors such as accelerometers,goniometers, etc. that may or may not be implanted. For example, in anexample embodiment one or more external sensors are connected to thesystem wirelessly. In some embodiments, data sensed by these sensorscontributes to (or comprises) the system interaction model on whichbasis the user can define stimulation goals (as described above). Insome embodiments, the data sensed by these sensors alternatively oradditionally provides a sensed system state, for example, for theclosed-loop control.

Thus, for example, according to an example, a method includes a systemcontrolling electrodes of an implanted leadwire to perform aneuromodulation therapy according to a set of stimulation parameters,obtaining sensor readings from the electrodes indicating a patientstate, and, responsive to the state indicated by the sensor readings andbased on the user-input state information, modifying the stimulationparameters.

Further, according to an example, the system receives over time aplurality of user input of clinical states and correlates the user inputclinical states to biopotential signatures sensed by electrodes at timesto which the clinical states respectively correspond, where the sensorreadings responsive to which the stimulation parameters are modifiedindicate respective biopotential signatures, and the modification isbased on the correlation.

In an example embodiment of the invention, data from non-implantedsensors (e.g., a blood pressure sensor) are time-correlated to data fromimplanted sensors using an algorithm (e.g., a support vector machine orartificial neural network), such that the user can define the sensedsystem state via external sensors, and a surrogate sensed system statebased on sensing done by the implantable sensors can be defined (e.g.,automatically) and used in the absence of external sensing tools. Forexample, the signal from external sensors is correlated to a measurementby the implanted system, and if there is good correlation, then thesignal measured by the implanted system can be used instead. Thereafter,the sensed measurement from the electrodes of the implanted leadwire canbe used for the closed-loop control.

Some example sensor readings which can be correlated by the system withstates on which basis to modify the settings include mean firing rate,power readings at a specified frequency band, and local fieldpotentials.

In an example embodiment, the system is configured to provide aninterface via which a patient can define a state of being (e.g., a painscore, or a depression score, or a stress/anxiety score) at severalpoints in time, and the system searches via algorithms for a sensedsystem state via the implantable sensors that correlates with thepatient scores. That is, the system looks for signatures in the senseddata (e.g., biopotentials, such as voltages generated by biologicalsystems, e.g., LFPs and action potentials) that correlate with thepatient input about the patient's state. The sensor data and/or patientinput can be provided in one or more sessions where an algorithmcollects “training” data by which the biopotential signature-to-staterelationships are determined. In an example embodiment, the algorithm isa dynamic learning algorithm that continuously updates as new “training”data is obtained via patient feedback (these algorithms can beparticularly useful given the possibility and perhaps likelihood ofplastic change in the neural system over time). Stimulation can then beset to be responsive to the sensed system bipotential state. Thus, basedon the training data, the system associates biopotentials withparticular patient states, and thereafter, the system can change whichstimulation paradigms and/or stimulation settings in response to thebiopotential state. Automated algorithms for defining the sensed systemstate are particularly useful because they, as noted above, make asystem of such vast complexity usable in clinical practice.

For example, the system is configured to obtain from a patient andovertime, for each of a plurality of stimulation sessions, feedback,e.g., OK, Mild, Medium, Severe, etc., regarding particular symptoms,e.g., depression, stress, pain, etc., and, based on such feedback,update the preferred stimulation paradigm and parameters. Moreover, thefeedback can be associated with biopotentials, which biotentialsignatures can serve as system states with which different stimulationparadigms are associated, so that the system changes which paradigm andsettings are used at different sensed biopotentials. In an exampleembodiment, the system is configured to automatically stimulate usingthe updated preferred stimulation paradigm and parameters as long as thepreferred program is within user-defined parameter constraints.

In an example embodiment, the system uses multiple independent currentcontrol (i.e., multiple current sources), thereby providing for a morestable stimulation, and a more fine-tuned stimulation (enables “virtualcontacts” so that many stimulation sites can be defined with a fewernumbers of electrodes). The “virtual contacts” also enable higherresolution mapping of the neural system by centering stimulation atpositions of the leadwire at which an electrode does not exist.

As described above, according to an example, the present system uses apatient-specific interaction biopotential analytics-based model ortractographical model to output a suggested stimulation paradigm for aninput neuromodulation target. This significantly simplifies the settingby the clinician of stimulation parameters so that, according to exampleembodiments, the system can include one or more leadwires with numerouselectrodes, e.g., 32 or more electrodes, even as many as into thehundreds of electrodes, since it is not required for the clinician toselect which of the electrodes to use or the parameters to be usedwithout any guidance on the stimulation regions on which to focus.Instead, the system can generate the patient-specific interaction modelfor a system including many electrodes. The user can input the targetneuromodulation sites and effects, and, based on the model, the systemoutputs suggested combination of electrodes (at respective stimulationsites) and the respective simulation parameters for effecting the targetneuromodulation response. (It is noted that the suggested stimulationparadigm can include a sequence of stimulations using differentcombinations of electrodes at different settings at different pointsthroughout the sequence.)

Accordingly, FIG. 1 shows components of a system according to an exampleembodiment of the present invention, including an IPG with four portsA-D, each of which is shown with a wired connection to a respective setof 8 contacts. The respective eight contacts to which ports A and B areconnected are on respective percutaneous cylindrical leads that areimplanted, e.g., into the brain. The two sets of eight contacts to whichports C and D are connected are shown to be on a single paddle lead,i.e., the paddle lead includes 16 electrodes to which the IPG isconnected via ports C and D. The paddle lead can be, according to anexample embodiment, adapted for implantation on top of the brain.

FIG. 2 shows another example embodiment including 22 leadwires with atotal of 176 electrodes, where the IPG is adapted for controlling 32 ofthe electrodes at any one time. The illustrated IPG includes four portsA-D each providing 8 inputs to a respective multiplexing module. Each ofthe multiplexing modules selects which of up to 8 electrodes, of aplurality of electrodes to which the respective multiplexing module isconnected, to control based on the input from the IPG. This embodimentassumes that the system would select between various combinations of the176 electrodes for achieving a target, where any output paradigm wouldinclude stimulation using, at any one time, only up to 32 electrodes.The paradigm can, however, include a sequence where differentcombinations of up to 32 electrodes from the 176 electrodes are used atdifferent times, the paradigm potentially making use of all 176electrodes. Additionally, these numbers are provided by way of example.Other embodiments may include a different number of total electrodes andmay allow for a different number of the electrodes to be usedsimultaneously, e.g., more than 32.

Thus, according to an example embodiment of the present invention, astimulation system includes an implanted pulse generator (IPG) includinga plurality of ports; a plurality of leadwires, each of the leadwiresincluding at least one respective electrode; and at least onemultiplexer between a respective one of the ports and a respectivesubset of more than one of the plurality of leadwires, which multiplexeris configured to select between the subset of the more than one of theplurality of leadwires for application to the electrodes thereof, of asignal from the respective port, the signal causing the electrodesthereof to produce electrical pulses for neuromodulation of tissue inwhich the subset of leadwires is implanted.

FIG. 4 shows information captured in a tractography map, which can beused to understand relationships between different parts of brain byanalyzing fiber paths from the image, as discussed above. FIG. 5 showsan example probabilistic tractographically based stimulation toneuromodulation mapping. For example, in an example embodiment, based ona tractography map, such as that shown in FIG. 4, the system determinesthat a stimulation at one region is expected to cause a neuromodulationresponse at another region (and vice versa) because of the extension ofa neural element between those two regions. As described above, aprobabilistic tractographic interaction model can be used instead of abiopotential response based model.

In an example embodiment, the probabilistic tractographical neuromodulation response mapping is based on a tractography map, such as oneshown in FIG. 4, alone. In an alternative example embodiment, theprobabilistic tractographical neuro modulation response mapping isadditionally based on neural modeling including a model of a producedelectric field in a patient anatomy at particular stimulation parametersand where such an electric field reaches a threshold for causing aneural activation. Combining such a neural model with a tractography mapprovides further refined data. Specifically, according to an exampleembodiment, the system uses the neural model to determine which neuralelements would be activated at a particular stimulation paradigm, andthen uses the tractography map to determine the neuromodulation sitesthat would be affected by those activated neural elements. For example,if the system determines from the neural model that site A would beactivated, and determines that a particular neural element located atsite A extends to site B, the system would estimate that site B wouldalso be activated.

The above description is intended to be illustrative, and notrestrictive. Those skilled in the art can appreciate from the foregoingdescription that the present invention may be implemented in a varietyof forms, and that the various embodiments may be implemented alone orin combination. Therefore, while the embodiments of the presentinvention have been described in connection with particular examplesthereof, the true scope of the embodiments and/or methods of the presentinvention should not be so limited since other modifications will becomeapparent to the skilled practitioner upon a study of the drawings,specification, and the following claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computer processor, user input selecting aneuromodulation effect region of a patient; and responsive to the input,determining, by the processor, electrode neuromodulation settingsestimated by the processor to, when applied to an implanted electrodeleadwire, produce a volume of tissue activation that at least partiallyencompasses at least one target anatomical stimulation candidate regionwhich is mapped by the processor to, and is at a distance from, theselected neuromodulation effect region; wherein at least one of: theleadwire is implanted in the patient's brain; the at least one targetanatomical stimulation candidate region includes a plurality of distinctregions; the user input specifies the neuromodulation effect region as aregion at which neural activity is to be reduced; and the mapping atleast one of: is based on a susceptibility weighted imaging (SWI) image;is based on a probabilistic tractography model; and includes apatient-specific mapping that is specific to the patient and that isbased on response information indicating prior responses by the patientto previous neuromodulations.
 2. The method of claim 1, wherein theleadwire is implanted in the patient's brain.
 3. The method of claim 1,wherein the at least one target anatomical stimulation candidate regionincludes the plurality of distinct regions.
 4. The method of claim 1,wherein the user input specifies the neuromodulation effect region asthe region at which neural activity is to be reduced.
 5. The method ofclaim 1, wherein the mapping is based on the SWI image.
 6. The method ofclaim 1, wherein the mapping is based on the probabilistic tractographymodel.
 7. The method of claim 1, wherein the mapping includes thepatient-specific mapping that is specific to the patient and that isbased on the response information indicating prior responses by thepatient to previous neuromodulations.
 8. The method of claim 1, whereinthe selected neuromodulation effect region is one of a plurality ofneuromodulation effect regions selected by the user input, and thedetermining includes determining one or more sets of neuromodulationsettings to be distributed to one or more electrodes of the leadwire toproduce a selected therapeutic effect at the plurality ofneuromodulation effect regions.
 9. The method of claim 1, wherein themapping performed by the processor is performed by extrapolating newstimulation parameters from modeled stimulation parameters that aremapped in the model to neuromodulation effect regions, which stimulationparameters are estimated, by the processor and based on the model, toproduce a selected effect at the selected neuromodulation effect region,the selected effect at the selected neuromodulation effect region notbeing mapped in the model.
 10. The method of claim 1, further comprisinggenerating the mapping, the generation of the mapping including:performing a plurality of neuromodulations at a respective plurality ofsets of neuromodulation settings; and recording, for each of at least asubset of the neurmodulations, at least one neuromodulation effectoccurring at a respective one or more anatomical regions, the one ormore anatomical region being responsively mapped by the processor to arespective one or more stimulation regions of tissue estimated by theprocessor to have been activated by the respective neuromodulation. 11.The method of claim 10, wherein the mapping is the patient-specificmapping and the plurality of neuromodulations are performed on thepatient.
 12. The method of claim 10, wherein at least some of theneuromodulation effects are sensed by electrodes of the leadwire. 13.The method of claim 12, wherein the plurality of neuromodulations areperformed by electrodes of the leadwire different than the electrodesthat sensed the at least some of the neuromodulation effects.
 14. Themethod of claim 12, wherein each of at least a subset of the electrodesof the leadwire are configured to both perform a neuromodulation andsense an effect of a neuromodulation.
 15. The method of claim 10,wherein at least some of the neuromodulation effects are input by auser.
 16. The method of claim 10, wherein the leadwire includes morethan 16 electrodes.
 17. The method of claim 16, wherein the leadwire iscontrolled via an implanted pulse generator that is configured to cause32 electrodes of the leadwire to simultaneously output an electricalstimulation pulse.
 18. The method of claim 16, wherein the leadwire isone of a plurality of leadwires that are all controlled via an implantedpulse generator that is configured to cause 32 electrodes of theplurality of leadwires to simultaneously output an electricalstimulation pulse.
 19. A computer-implemented method, comprising:controlling electrodes of an implanted leadwire to perform aneuromodulation therapy according to a set of stimulation parameters;obtaining sensor readings from the electrodes; and responsive to thesensor readings, modifying the stimulation parameters.
 20. The method ofclaim 19, further comprising: receiving over time a plurality of userinput of clinical states; and correlating, by the processor, the userinput clinical states to biopotential signatures sensed by electrodes attimes to which the clinical states respectively correspond; wherein thesensor readings responsive to which the stimulation parameters aremodified indicate respective biopotential signatures, and themodification is based on the correlation.
 21. The method of claim 19,further comprising: receiving user input of a plurality of states, and,for each of the plurality of states, a respective set of stimulationparameters to be applied responsive to sensing of the respective state,wherein at least one of the states is defined according to a reading ofa non-implanted sensor; time-correlating, by the processor, readings ofthe electrodes of the leadwire to readings of the non-implanted sensor;and substituting, by the processor, the definition of the at least oneof the states with an alternate definition of a state according to areading of the electrodes of the leadwire; wherein the modifying isperformed based on the alternate definition.
 22. The method of claim 19,wherein the modification is based on sensor readings of a plurality ofthe electrodes positioned at a respective plurality of anatomicallocations.
 23. The method of claim 19, wherein the sensor readingsindicate a mean firing rate of neural elements, the modification beingbased on the mean firing rate.
 24. The method of claim 19, wherein thesensor readings are power readings at a specified frequency band. 25.The method of claim 19, wherein the sensor readings are of local fieldpotentials.
 26. A stimulation system comprising: an implanted pulsegenerator (IPG) including a plurality of ports; a plurality ofleadwires, each of the leadwires including at least one respectiveelectrode; and at least one multiplexer between a respective one of theports and a respective subset of more than one of the plurality ofleadwires, which multiplexer is configured to select between the subsetof the more than one of the plurality of leadwires for application tothe electrodes thereof, of a signal from the respective port, the signalcausing the electrodes thereof to produce electrical pulses forneuromodulation of tissue in which the subset of leadwires is implanted.